Learning to Rank Personalized Search Results in Professional Networks

TL;DR

This paper introduces a personalized learning-to-rank approach for LinkedIn search, leveraging user intent inference and homophily to improve result relevance based on individual searcher profiles.

Contribution

It presents a novel method combining intent inference and homophily with learning-to-rank for personalized search results in professional networks.

Findings

Improved search relevance through personalization

Effective inference of user search intents

Enhanced ranking performance with combined signals

Abstract

LinkedIn search is deeply personalized - for the same queries, different searchers expect completely different results. This paper presents our approach to achieving this by mining various data sources available in LinkedIn to infer searchers' intents (such as hiring, job seeking, etc.), as well as extending the concept of homophily to capture the searcher-result similarities on many aspects. Then, learning-to-rank (LTR) is applied to combine these signals with standard search features.